# STAT2

## First EditionNew Edition Available Ann R. Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore; Allan J. Rossman; Jeffrey A. Witmer

©2014*offers students who have taken AP Statistics or a typical introductory statistics college level course to learn more sophisticated concepts and the tools with which to apply them.*

**STAT2**The authors' primary goal is to help students gain facility in the use of common statistical models. The text instructs students on working with models where the response variable is either quantitative or categorical and predictors (or explanatory factors) are quantitative or categorical (or both). The chapters are grouped to consider models based on the type of response and type of predictors.

After completing a course with

1. Choose the appropriate statistical model for a particular problem.

2. Know the conditions that are typically required when fitting various models.

3. Assess whether or not the conditions for a particular model are reasonably met for a specific dataset. 4. Have some strategies for dealing with data when the conditions for a standard model are not met.

5. Use the appropriate model to make appropriate inferences.

*students should be able to:***STAT2**1. Choose the appropriate statistical model for a particular problem.

2. Know the conditions that are typically required when fitting various models.

3. Assess whether or not the conditions for a particular model are reasonably met for a specific dataset. 4. Have some strategies for dealing with data when the conditions for a standard model are not met.

5. Use the appropriate model to make appropriate inferences.

## Table of Contents

**0 What Is a Statistical Model? **0.1 Fundamental Terminology

0.2 Four-Step Process

0.3 Chapter Summary

0.4 Exercises

**Unit A: Linear Regression**

**1 Simple Linear Regression **1.1 The Simple Linear Regression Model

1.2 Conditions for a Simple Linear Model

1.3 Assessing Conditions

1.4 Transformations

1.5 Outliers & Influential Points

1.6 Chapter Summary

1.7 Exercises

**2 Inference for Simple Linear Regression **2.1 Inference for Regression Slope

2.2 Partitioning Variability - ANOVA

2.3 Regression and Correlation

2.4 Intervals for Predictions

2.5 Chapter Summary

2.6 Exercises

**3 Multiple Regression **3.1 Multiple Linear Regression Model

3.2 Assessing a Multiple Regression Model

3.3 Comparing Two Regression Lines

3.4 New Predictors from Old

3.5 Correlated Predictors

3.6 Testing Subsets of Predictors

3.7 Case Study: Predicting in Retail Clothing

3.8 Chapter Summary

3.9 Exercises

**4 Additional Topics in Regression **4.1 Topic: Added Variable Plots

4.2 Topic: Techniques for Choosing Predictors

4.3 Topic: Identifying Unusual Points in Regression

4.4 Topic: Coding Categorical Predictors

4.5 Topic: Randomization Test for a Relationship

4.6 Topic: Bootstrap for Regression

4.7 Exercises

**Unit B: Analysis of Variance**

**5 One-way ANOVA **5.1 The One-way Model: Comparing Groups

5.2 Assessing and Using the Model

5.3 Scope of Inference

5.4 Fisher’s Least Significant Difference

5.5 Chapter Summary

5.6 Exercises

**6 Multifactor ANOVA **6.1 The Two-way Additive Model (Main Effects Model)

6.2 Interaction in the Two-way Model

6.3 The Two-way Non-additive Model (Two-Way ANOVA with Interaction)

6.4 Case Study

6.5 Chapter Summary

6.6 Exercises

**7 Additional Topics in Analysis of Variance **7.1 Topic: Levene’s Test for Homogeneity of Variances

7.2 Topic: Multiple Tests

7.3 Topic: Comparisons and Contrasts

7.4 Topic: Nonparametric Statistics

7.5 Topic: ANOVA and Regression with Indicators

7.6 Topic: Analysis of Covariance

7.7 Exercises

**8 Overview of Experimental Design**8.1 Comparisons and Randomization

8.2 Randomization F Test

8.3 Design Strategy: Blocking

8.4 Design Strategy: Factorial Crossing

8.5 Chapter Summary

8.6 Exercises

**Unit C: Logistic Regression**

**9 Logistic Regression **9.1 Choosing a Logistic Regression Model

9.2 Logistic regression and odds ratios

9.3 Assessing the logistic regression model

9.4 Formal inference: tests and intervals

9.5 Summary

9.6 Exercises

**10 Multiple Logistic Regression **10.1 Overview

10.2 Choosing, fitting, and interpreting models

10.3 Checking conditions

10.4 Formal inference: tests and intervals

10.5 Case Study: Bird Nests

10.6 Summary

10.7 Exercises

**11 Additional Topics in Logistic Regression **11.1 Topic: Fitting the logistic regression model

11.2 Topic: Assessing Logistic Regression Models

11.3 Randomization Tests

11.4 Analyzing Two-way Tables with Logistic Regression

11.5 Exercises